Machine Learning and Interpretability Study for Predicting 30-Day Unplanned Readmission Risk of Schizophrenia: A Retrospective Study.
An XGB risk prediction model can accurately evaluate the 30-day UPR of patients with schizophrenia. Combined with SHAP, it can provide patients with personalized risk predictions, thereby assisting medical staff in achieving early discharge plans and transitional care.
Authors
- Xingxin Zhan (ORCID: https://orcid.org/0000-0003-0277-5427)
- Le Qiao (ORCID: https://orcid.org/0000-0002-6180-2344)
- Zhixia Zhang (ORCID: https://orcid.org/0000-0002-1657-0636)
- Yuting Tan (ORCID: https://orcid.org/0009-0002-1949-7937)
- Rong Chao Cheng (ORCID: https://orcid.org/0009-0000-3613-6020)
- Yaping Liu (ORCID: https://orcid.org/0000-0002-2015-8775)
- Guiling Chen (ORCID: https://orcid.org/0000-0001-5170-5324)
- S J Wang
Institutions
- Xinyu University (CN)
- Wuhan University of Science and Technology (CN)
Publication Details
- Journal
- PubMed
- Published
- 2025-01-01
- DOI
- https://doi.org/10.2147/ndt.s522675
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00